A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Journal: Nature cancer
PMID: 38443662

Abstract

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor's genetic profile modulates CDK4/6i resistance.

Authors

  • Sungjoon Park
    Department of Computer Science and Engineering, Korea University, Seoul, South Korea.
  • Erica Silva
    Program in Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA.
  • Akshat Singhal
    Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
  • Marcus R Kelly
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Kate Licon
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Isabella Panagiotou
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Catalina Fogg
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Samson Fong
    Department of Medicine, University of California San Diego, La Jolla, California, USA.
  • John J Y Lee
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Xiaoyu Zhao
    Department of Science and Technology, Hebei Agricultural University, Huanghua, China.
  • Robin Bachelder
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Barbara A Parker
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Kay T Yeung
    Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  • Trey Ideker

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